Upload script and readme

README.md

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+---
+annotations_creators:
+- crowdsourced
+- machine-generated
+language_creators:
+- crowdsourced
+- machine-generated
+language:
+- en
+license:
+- cc-by-2.0
+- cc-by-2.5
+- cc-by-3.0
+- cc-by-4.0
+- cc-by-sa-3.0
+- cc-by-sa-4.0
+multilinguality:
+- monolingual
+pretty_name: People's Speech
+size_categories:
+- 1T<n
+source_datasets:
+- original
+task_categories:
+- automatic-speech-recognition
+task_ids:
+- speech-recognition
+- robust-speech-recognition
+- noisy-speech-recognition
+---
+
+# Dataset Card for People's Speech
+
+## Table of Contents
+- [Dataset Description](#dataset-description)
+  - [Dataset Summary](#dataset-summary)
+  - [Supported Tasks](#supported-tasks-and-leaderboards)
+  - [Languages](#languages)
+- [Dataset Structure](#dataset-structure)
+  - [Data Instances](#data-instances)
+  - [Data Fields](#data-instances)
+  - [Data Splits](#data-instances)
+- [Dataset Creation](#dataset-creation)
+  - [Curation Rationale](#curation-rationale)
+  - [Source Data](#source-data)
+  - [Annotations](#annotations)
+  - [Personal and Sensitive Information](#personal-and-sensitive-information)
+- [Considerations for Using the Data](#considerations-for-using-the-data)
+  - [Social Impact of Dataset](#social-impact-of-dataset)
+  - [Discussion of Biases](#discussion-of-biases)
+  - [Other Known Limitations](#other-known-limitations)
+- [Additional Information](#additional-information)
+  - [Dataset Curators](#dataset-curators)
+  - [Licensing Information](#licensing-information)
+  - [Citation Information](#citation-information)
+
+## Dataset Description
+
+- **Homepage:** https://mlcommons.org/en/peoples-speech/
+- **Repository:** https://github.com/mlcommons/peoples-speech
+- **Paper:** https://arxiv.org/abs/2111.09344
+- **Leaderboard:** [Needs More Information]
+- **Point of Contact:** [datasets@mlcommons.org](mailto:datasets@mlcommons.org)
+
+### Dataset Summary
+
+The People's Speech Dataset is among the world's largest English speech recognition corpus today that is licensed for academic and commercial usage under CC-BY-SA and CC-BY 4.0. It includes 30,000+ hours of transcribed speech in English languages with a diverse set of speakers. This open dataset is large enough to train speech-to-text systems and crucially is available with a permissive license.
+
+### Supported Tasks and Leaderboards
+
+[Needs More Information]
+
+### Languages
+
+English
+
+## Dataset Structure
+
+### Data Instances
+
+{
+    "id": "gov_DOT_uscourts_DOT_scotus_DOT_19-161/gov_DOT_uscourts_DOT_scotus_DOT_19-161_DOT_2020-03-02_DOT_mp3_00002.flac",
+    "audio": {
+        "path": "gov_DOT_uscourts_DOT_scotus_DOT_19-161/gov_DOT_uscourts_DOT_scotus_DOT_19-161_DOT_2020-03-02_DOT_mp3_00002.flac"
+        "array": array([-6.10351562e-05, ...]),
+        "sampling_rate": 16000
+    }
+    "duration_ms": 14490,
+    "text": "contends that the suspension clause requires a [...]"
+}
+
+### Data Fields
+
+{
+    "id": datasets.Value("string"),
+    "audio": datasets.Audio(sampling_rate=16_000),
+    "duration_ms": datasets.Value("int32"),
+    "text": datasets.Value("string"),
+}
+
+### Data Splits
+
+We provide the following configurations for the dataset: `cc-by-clean`, `cc-by-dirty`, `cc-by-sa-clean`, `cc-by-sa-dirty`, and `microset`. We don't provide splits for any of the configurations.
+
+## Dataset Creation
+
+### Curation Rationale
+
+See our [paper](https://arxiv.org/abs/2111.09344).
+
+### Source Data
+
+#### Initial Data Collection and Normalization
+
+Data was downloaded via the archive.org API. No data inference was done.
+
+#### Who are the source language producers?
+
+[Needs More Information]
+
+### Annotations
+
+#### Annotation process
+
+No manual annotation is done. We download only source audio with already existing transcripts.
+
+#### Who are the annotators?
+
+For the test and dev sets, we paid native American English speakers to do transcriptions. We do not know the identities of the transcriptionists for data in the training set. For the training set, we have noticed that some transcriptions are likely to be the output of automatic speech recognition systems. 
+
+### Personal and Sensitive Information
+
+Several of our sources are legal and government proceedings, spoken histories, speeches, and so on. Given that these were intended as public documents and licensed as such, it is natural that the involved individuals are aware of this.
+
+## Considerations for Using the Data
+
+### Social Impact of Dataset
+
+The dataset could be used for speech synthesis. However, this requires careful cleaning of the dataset, as background noise is not tolerable for speech synthesis.
+
+The dataset could be used for keyword spotting tasks as well. In particular, this is good use case for the non-English audio in the dataset.
+
+Our sincere hope is that the large breadth of sources our dataset incorporates reduces existing quality of service issues today, like speech recognition system’s poor understanding of non-native English accents. We cannot think of any unfair treatment that come from using this dataset at this time.
+
+
+### Discussion of Biases
+
+Our data is downloaded from archive.org. As such, the data is biased towards whatever users decide to upload there.
+
+Almost all of our data is American accented English.
+
+### Other Known Limitations
+
+As of version 1.0, a portion of data in the training, test, and dev sets is poorly aligned. Specifically, some words appear in the transcript, but not the audio, or some words appear in the audio, but not the transcript. We are working on it.
+
+## Additional Information
+
+### Dataset Curators
+
+[Needs More Information]
+
+### Licensing Information
+
+We provide CC-BY and CC-BY-SA subsets of the dataset.
+
+### Citation Information
+
+Please cite:
+
+```
+@article{DBLP:journals/corr/abs-2111-09344,
+  author    = {Daniel Galvez and
+               Greg Diamos and
+               Juan Ciro and
+               Juan Felipe Cer{\'{o}}n and
+               Keith Achorn and
+               Anjali Gopi and
+               David Kanter and
+               Maximilian Lam and
+               Mark Mazumder and
+               Vijay Janapa Reddi},
+  title     = {The People's Speech: {A} Large-Scale Diverse English Speech Recognition
+               Dataset for Commercial Usage},
+  journal   = {CoRR},
+  volume    = {abs/2111.09344},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2111.09344},
+  eprinttype = {arXiv},
+  eprint    = {2111.09344},
+  timestamp = {Mon, 22 Nov 2021 16:44:07 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2111-09344.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+```

peoples_speech.py

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+# Copyright 2020 The HuggingFace Datasets Authors and the current dataset script contributor.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import json
+import os
+
+import datasets
+from datasets.tasks import AutomaticSpeechRecognition
+from tqdm.auto import tqdm
+
+
+# Find for instance the citation on arxiv or on the dataset repo/website
+_CITATION = """\
+@article{DBLP:journals/corr/abs-2111-09344,
+  author    = {Daniel Galvez and
+               Greg Diamos and
+               Juan Ciro and
+               Juan Felipe Ceron and
+               Keith Achorn and
+               Anjali Gopi and
+               David Kanter and
+               Maximilian Lam and
+               Mark Mazumder and
+               Vijay Janapa Reddi},
+  title     = {The People's Speech: A Large-Scale Diverse English Speech Recognition
+               Dataset for Commercial Usage},
+  journal   = {CoRR},
+  volume    = {abs/2111.09344},
+  year      = {2021},
+  url       = {https://arxiv.org/abs/2111.09344},
+  eprinttype = {arXiv},
+  eprint    = {2111.09344},
+  timestamp = {Mon, 22 Nov 2021 16:44:07 +0100},
+  biburl    = {https://dblp.org/rec/journals/corr/abs-2111-09344.bib},
+  bibsource = {dblp computer science bibliography, https://dblp.org}
+}
+"""
+
+# You can copy an official description
+_DESCRIPTION = """\
+The People's Speech is a free-to-download 30,000-hour and growing supervised 
+conversational English speech recognition dataset licensed for academic and 
+commercial usage under CC-BY-SA (with a CC-BY subset).
+"""
+
+_HOMEPAGE = "https://mlcommons.org/en/peoples-speech/"
+
+_LICENSE = [
+    "cc-by-2.0", "cc-by-2.5", "cc-by-3.0", "cc-by-4.0", "cc-by-sa-2.5",
+    "cc-by-sa-3.0", "cc-by-sa-4.0"
+]
+
+_BASE_URL = "https://huggingface.co/datasets/MLCommons/peoples_speech/resolve/main/"
+
+# relative path to data inside dataset's repo
+_DATA_URL = _BASE_URL + "{split}/{config}/{config}_{archive_id:06d}.tar"
+
+# relative path to file containing number of audio archives inside dataset's repo
+_N_FILES_URL = _BASE_URL + "{split}/{config}/n_files.txt"
+
+# relative path to metadata inside dataset's repo
+_MANIFEST_URL = _BASE_URL + "{split}/{config}.json"
+
+
+class PeoplesSpeech(datasets.GeneratorBasedBuilder):
+    """The People's Speech dataset."""
+
+    VERSION = datasets.Version("1.1.0")
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(name="microset", version=VERSION, description="Small subset of clean data for example pusposes."),
+        datasets.BuilderConfig(name="clean", version=VERSION, description="Clean, CC-BY licensed subset."),
+        datasets.BuilderConfig(name="dirty", version=VERSION, description="Dirty, CC-BY licensed subset."),
+        datasets.BuilderConfig(name="clean_sa", version=VERSION, description="Clean, CC-BY-SA licensed subset."),
+        datasets.BuilderConfig(name="dirty_sa", version=VERSION, description="Dirty, CC-BY-SA licensed subset."),
+    ]
+    DEFAULT_CONFIG_NAME = "clean"
+    DEFAULT_WRITER_BATCH_SIZE = 1
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "id": datasets.Value("string"),
+                    "audio": datasets.Audio(sampling_rate=16_000),
+                    "duration_ms": datasets.Value("int32"),
+                    "text": datasets.Value("string"),
+                }
+            ),
+            task_templates=[AutomaticSpeechRecognition()],
+            supervised_keys=("file", "text"),
+            homepage=_HOMEPAGE,
+            license="/".join(_LICENSE),  # license must be a string
+            citation=_CITATION,
+        )
+
+    def _get_n_files(self, dl_manager, split, config):
+        n_files_url = _N_FILES_URL.format(split=split, config=config)
+        n_files_path = dl_manager.download_and_extract(n_files_url)
+
+        with open(n_files_path, encoding="utf-8") as f:
+            return int(f.read().strip())
+
+    def _split_generators(self, dl_manager):
+
+        if self.config.name == "microset":
+            # take only first data archive for demo purposes
+            url = [_DATA_URL.format(split="train", config="clean", archive_id=0)]
+            archive_path = dl_manager.download(url)
+            local_extracted_archive_path = dl_manager.extract(archive_path) if not dl_manager.is_streaming else [None]
+            manifest_url = _MANIFEST_URL.format(split="train", config="clean_000000")  # train/clean_000000.json
+            manifest_path = dl_manager.download_and_extract(manifest_url)
+
+            return [
+                datasets.SplitGenerator(
+                    name=datasets.Split.TRAIN,
+                    gen_kwargs={
+                        "local_extracted_archive_paths": local_extracted_archive_path,
+                        # use iter_archive here to access the files in the TAR archives:
+                        "archives": [dl_manager.iter_archive(path) for path in archive_path],
+                        "manifest_path": manifest_path,
+                    },
+                ),
+            ]
+
+        n_files_train = self._get_n_files(dl_manager, split="train", config=self.config.name)
+        n_files_dev = self._get_n_files(dl_manager, split="dev", config="dev")
+        n_files_test = self._get_n_files(dl_manager, split="test", config="test")
+
+        urls = {
+            "train": [_DATA_URL.format(split="train", config=self.config.name, archive_id=i) for i in range(n_files_train)],
+            "dev": [_DATA_URL.format(split="dev", config="dev", archive_id=i) for i in range(n_files_dev)],
+            "test": [_DATA_URL.format(split="test", config="test", archive_id=i) for i in range(n_files_test)],
+        }
+        archive_paths = dl_manager.download(urls)
+
+        # In non-streaming mode, we extract the archives to have the data locally:
+        local_extracted_archive_paths = dl_manager.extract(archive_paths) if not dl_manager.is_streaming else \
+            {
+                "train": [None] * len(archive_paths),
+                "dev": [None] * len(archive_paths),
+                "test": [None] * len(archive_paths),
+            }
+
+        manifest_urls = {
+            "train": _MANIFEST_URL.format(split="train", config=self.config.name),
+            "dev": _MANIFEST_URL.format(split="dev", config="dev"),
+            "test": _MANIFEST_URL.format(split="test", config="test"),
+        }
+        manifest_paths = dl_manager.download_and_extract(manifest_urls)
+
+        # To access the audio data from the TAR archives using the download manager,
+        # we have to use the dl_manager.iter_archive method
+        #
+        # This is because dl_manager.download_and_extract
+        # doesn't work to stream TAR archives in streaming mode.
+        # (we have to stream the files of a TAR archive one by one)
+        #
+        # The iter_archive method returns an iterable of (path_within_archive, file_obj) for every
+        # file in a TAR archive.
+
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "local_extracted_archive_paths": local_extracted_archive_paths["train"],
+                    # use iter_archive here to access the files in the TAR archives:
+                    "archives": [dl_manager.iter_archive(path) for path in archive_paths["train"]],
+                    "manifest_path": manifest_paths["train"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.VALIDATION,
+                gen_kwargs={
+                    "local_extracted_archive_paths": local_extracted_archive_paths["dev"],
+                    # use iter_archive here to access the files in the TAR archives:
+                    "archives": [dl_manager.iter_archive(path) for path in archive_paths["dev"]],
+                    "manifest_path": manifest_paths["dev"],
+                },
+            ),
+            datasets.SplitGenerator(
+                name=datasets.Split.TEST,
+                gen_kwargs={
+                    "local_extracted_archive_paths": local_extracted_archive_paths["dev"],
+                    # use iter_archive here to access the files in the TAR archives:
+                    "archives": [dl_manager.iter_archive(path) for path in archive_paths["test"]],
+                    "manifest_path": manifest_paths["test"],
+                },
+            ),
+        ]
+
+    def _generate_examples(self, local_extracted_archive_paths, archives, manifest_path):
+        meta = dict()
+        with open(manifest_path, "r", encoding="utf-8") as f:
+            for line in tqdm(f, desc="reading metadata file"):
+                sample_meta = json.loads(line)
+                _id = sample_meta["audio_document_id"]
+                texts = sample_meta["training_data"]["label"]
+                audio_filenames = sample_meta["training_data"]["name"]
+                durations = sample_meta["training_data"]["duration_ms"]
+                for audio_filename, text, duration in zip(audio_filenames, texts, durations):
+                    audio_filename = audio_filename.lstrip("./")
+                    meta[audio_filename] = {
+                        "audio_document_id": _id,
+                        "text": text,
+                        "duration_ms": duration
+                    }
+
+        for local_extracted_archive_path, archive in zip(local_extracted_archive_paths, archives):
+            # Here we iterate over all the files within the TAR archive:
+            for audio_filename, audio_file in archive:
+                audio_filename = audio_filename.lstrip("./")
+                # if an audio file exists locally (i.e. in default, non-streaming mode) set the full path to it
+                # joining path to directory that the archive was extracted to and audio filename.
+                path = os.path.join(local_extracted_archive_path, audio_filename) if local_extracted_archive_path \
+                    else audio_filename
+                yield audio_filename, {
+                    "id": audio_filename,
+                    "audio": {"path": path, "bytes": audio_file.read()},
+                    "text": meta[audio_filename]["text"],
+                    "duration_ms": meta[audio_filename]["duration_ms"]
+                }